Dust removal device and dust removal method for flue gas electric heater

Abstract

The invention discloses a dust removal device and method for a flue gas electric heater. The device comprises a flue gas inlet pipe, an atomization box, a dust pre-agglomeration box, an ultrasonic electric field composite dust remover and a flue gas exhaust pipe which are sequentially connected in the airflow direction. A first ultrasonic transmitter is arranged in the dust pre-agglomeration box, and the ultrasonic electric field composite dust remover comprises an electric dust remover and a second ultrasonic transmitter arranged in a dust removal chamber of the electric dust remover; a gas flow controller, a humidity sensor, a temperature sensor and a gas inlet dust sensor are arranged on the flue gas inlet pipe, and a gas exhaust dust sensor is arranged on the flue gas exhaust pipe. On one hand, related process parameters can be intelligently adjusted and optimized according to the flue gas property, and on the other hand, whether ash removal is needed or not can be judged through the characteristic indexes capable of directly reflecting the ash deposition condition of the electric dust remover, so that ash removal can be conducted in time, and the situation that the dust removal effect is reduced due to excessive ash deposition is avoided.

Description

Technical Field

[0001] The invention relates to the field of dust removal equipment, and in particular to a dust removal device and a dust removal method for a flue gas electric heater. Background Art

[0002] Flue gas is a widely generated waste gas in industrial production. It usually contains a large amount of dust and other pollutants and needs to be purified before it can be discharged. In the flue gas purification process, a lot of flue gas needs to be heated to increase the temperature before subsequent treatment. The flue gas electric heater is used to heat the flue gas to the required temperature. Before heating, the flue gas needs to be dust-removed to reduce the adverse effects of the flue gas on the electric heater. For example, dust will increase energy consumption and damage to equipment.

[0003] The electrostatic precipitator is a widely used dust removal equipment. When the flue gas passes through the flue in front of the main structure of the electrostatic precipitator, the smoke dust is positively charged, and then the smoke enters the electrostatic precipitator channel with multiple layers of cathode plates. Due to the mutual adsorption between the positively charged smoke dust and the cathode plate, the particles in the smoke are adsorbed on the cathode, and the cathode plate is hit regularly, so that the smoke dust with a certain thickness falls into the ash hopper under the electrostatic precipitator structure under the dual effects of self-weight and vibration, thereby achieving the purpose of removing smoke dust in the flue gas.

[0004] Traditional electrostatic precipitators have the following shortcomings: small-sized dust in flue gas is light in weight and has a small charge under the electric field. It is easily carried away by the airflow and is not easily collected by the electric field, resulting in a low removal rate. In addition, when cleaning, the equipment is easily damaged when the dust is shaken off by mechanical impact. And for the time of cleaning, a fixed time is usually used for cleaning, which cannot be well judged according to the actual dust accumulation of the electrostatic precipitator, resulting in untimely or unnecessary cleaning.

[0005] Ultrasonic dust removal has been applied. For example, patent CN108939787B discloses a flue gas acoustic wet-electric targeted dust removal method and system, which introduces a targeted polymer carrier and utilizes the acoustic agglomeration process to directionally agglomerate tiny particles in the flue gas, especially 0.3-0.6μm particles, and then removes them to achieve complete purification and emission of flue gas. Patent CN114432801A discloses a flue gas dust removal device, a flue gas emission purification system and method, and patent CN115006952B discloses an ultrasonic dust removal device. However, these solutions do not take into account the impact of ultrasonic parameters, flue gas properties, etc. on the process, and cannot intelligently adjust and optimize related process parameters based on flue gas properties.

[0006] Patent CN214811731U discloses an electric dust collector with ultrasonic dust removal, which uses ultrasonic dust removal to effectively avoid damage to the equipment. However, like the traditional solution, the dust removal time is also fixed, and there is still a problem of untimely or unnecessary dust removal.

[0007] Therefore, it is necessary to improve the existing technology to provide a more reliable solution. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a dust removal device and a dust removal method for a flue gas electric heater in view of the deficiencies in the above-mentioned prior art.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows: In a first aspect of the present invention, a dust removal device for a flue gas electric heater is provided, comprising: a flue gas inlet pipe, an atomization box, a dust pre-agglomeration box, an ultrasonic electric field composite dust collector and a flue gas exhaust pipe connected in sequence along the air flow direction;

[0010] A first ultrasonic transmitter is arranged in the dust pre-agglomeration box, and the ultrasonic electric field composite dust collector comprises an electrostatic precipitator and a second ultrasonic transmitter arranged in a dust removal chamber of the electrostatic precipitator;

[0011] The flue gas intake pipe is provided with a gas flow controller, a humidity sensor, a temperature sensor and an intake dust sensor, and the flue gas exhaust pipe is provided with an exhaust dust sensor.

[0012] Preferably, an atomizing nozzle is provided in the atomizing box, and the atomizing nozzle is connected to a water tank provided on the atomizing box through a water pipe for spraying atomized droplets into the atomizing box, and a water flow controller is provided on the water pipe.

[0013] Preferably, the electrostatic precipitator includes an electrostatic precipitator main unit, a discharge electrode and a dust collecting electrode arranged in the dust removal chamber, and a dust collecting hopper located below the electrostatic precipitator chamber, and the second ultrasonic transmitter is arranged in the electrostatic precipitator chamber.

[0014] Preferably, a plurality of guide channels are formed inside the dust pre-agglomeration box by a plurality of guide plates which are parallel to the axial direction of the dust pre-agglomeration box and spaced apart from each other, and both ends of the guide plates have conical tips.

[0015] Preferably, the dust removal device further comprises a controller connected to the gas flow controller, the humidity sensor, the temperature sensor, the intake dust sensor, the water flow controller, the first ultrasonic transmitter, the second ultrasonic transmitter, the electric dust removal host and the exhaust dust sensor;

[0016] The controller includes a parameter acquisition module, a parameter optimization model, a dust cleaning judgment module and a control module. The parameter optimization model is based on the characteristic parameter Fx of the flue gas introduced into the flue gas inlet pipe and the preset dust concentration target value C in the exhaust gas discharged from the flue gas exhaust pipe. m , using a machine learning algorithm to optimize the process parameter Gy of the dust removal device to obtain an optimized process parameter Gy', and the control module controls the water flow controller, the first ultrasonic transmitter, the second ultrasonic transmitter, and the electrostatic precipitator host according to Gy';

[0017] The characteristic parameters Fx of the flue gas include the flue gas flow rate Qw entering the dust removal device, the relative humidity RH of the flue gas, the temperature T, the dust concentration C in the flue gas, and the dust particle size distribution parameter Lc, which is recorded as Fx = (Qw, RH, T, C, Lc); wherein the dust particle size distribution parameter Lc includes the concentration C of PM1.0 in the dust 1.0 、PM2.5 concentration C 2.5 and PM10.0 concentration C 10.0 ;

[0018] The process parameters Gy of the dust removal device include the atomized droplet ratio coefficient K, the power P1 and frequency f1 of the first ultrasonic transmitter, the power P2 and frequency f2 of the second ultrasonic transmitter, and the working voltage U of the electrostatic precipitator host, which are denoted as Gy=(K, P1, f1, P2, f2, U);

[0019] The atomized droplet ratio coefficient K is the ratio of the water flow Qs introduced into the water delivery pipe to the smoke flow Qw introduced into the smoke inlet pipe, that is, K=Qs / Qw.

[0020] Preferably, the parameter optimization model performs process parameter optimization periodically, and the optimized process parameters are the characteristic parameter Fx of the currently introduced flue gas and the dust concentration target value C m In the case of max When , the total power consumption W reaches the minimum;

[0021] The total power consumption W is the sum of the power consumption W1 of the first ultrasonic transmitter, the power consumption W2 of the second ultrasonic transmitter and the power consumption W3 of the electrostatic precipitator host, that is, W=W1+W2+W3.

[0022] Preferably, the cleaning judgment module is used to judge whether it is necessary to clean the discharge electrode and the dust collecting electrode of the electrostatic precipitator. When cleaning is required, the control module controls the second ultrasonic transmitter to emit ultrasonic waves to clean the discharge electrode and the dust collecting electrode.

[0023] A second aspect of the present invention provides a dust removal method for a dust removal device for a flue gas electric heater as described above, comprising the following steps:

[0024] S1. During the operation of the dust removal device, the parameter optimization model performs process parameter optimization in a cycle: first, the parameter acquisition module obtains the following parameters of the flue gas currently entering the flue gas inlet pipe:

[0025] The flue gas flow rate Q detected by the gas flow controller t , relative humidity RH detected by humidity sensor t , the temperature T detected by the temperature sensor t And the dust concentration C detected by the intake dust sensor t and dust particle size distribution parameter Lc t , these parameters are used as the characteristic parameters Fx of the flue gas in the current cycle t t , denoted as Fx t =(Q t RH t , T t , C t , Lc t );

[0026] The parameter acquisition module then obtains the preset dust concentration target value C m ;

[0027] S2, the parameter acquisition module collects the characteristic parameters Fx of the flue gas t , Dust concentration target value C m Enter into the parameter optimization model to obtain the optimized process parameter Gy' in the current cycle t t , Gy' t =(K' t 、P1' t 、f1' t 、P2' t 、f2' t , U' t );

[0028] K' t represents the optimized atomized droplet ratio coefficient, P1' t 、f1' t Respectively represent the power and frequency of the first ultrasonic transmitter obtained by optimization, P2' t 、f2' t Respectively represent the optimized power and frequency of the second ultrasonic transmitter, U' t Indicates the optimized working voltage of the electrostatic precipitator host;

[0029] S3, the control module obtains the optimized process parameter Gy' according to the parameter optimization model t , control so that in the current cycle t:

[0030] The ratio of the water flow rate in the water pipe to the flue gas flow rate in the flue gas inlet pipe is K' t , the power of the first ultrasonic transmitter is P1' t , frequency is f1' t , the power of the second ultrasonic transmitter is P2' t , frequency is f2' t , the working voltage of the electrostatic precipitator host is U' t ;

[0031] S4. In the next cycle t+1, repeat steps S1-S3.

[0032] Preferably, the parameter optimization model is constructed by the following method:

[0033] S1-1. Construct training data set Z:

[0034] S1-1-1. The flue gas is introduced into the dust removal device for treatment. The flue gas flow rate Qw and the target dust concentration C in the exhaust gas are fixed. m ;

[0035] Get different relative humidity RH i , Temperature T i , Dust concentration C i And the dust particle size distribution parameter Lc i Under this condition, the actual dust concentration in the exhaust gas (detected by the exhaust dust sensor) is not higher than the dust concentration target value C m When the total power consumption W reaches the minimum, the process parameter Gy i , the total power consumption at this time is W i ;

[0036] Among them, the process parameter Gy i Including the atomized droplet ratio coefficient K i , the power P1 of the first ultrasonic transmitter i and frequency f1 i , the power of the second ultrasonic transmitter P2 i and frequency f2 i , Working voltage U of the electrostatic precipitator host i ;

[0037] Qw、C m RH i , T i , C i , Lc i and the corresponding Gy i , W i Combine to get training data z i, thereby obtaining the corresponding flue gas flow Qw and dust concentration target value C m The n training data under, i = 1, 2, ..., n, n = 10-100;

[0038] S1-1-2. Change the value of the flue gas flow rate Qw and the dust concentration target value C m , use the same method as step S1-1-1 to obtain m training data z j ,i=1,2,...,m,m=50-5000;

[0039] S1-1-3, all training data combinations are obtained to obtain a training data set Z;

[0040] S1-2. Model training:

[0041] Using the training data set Z, Qw, C m RH i , T i , C i , Lc i For access, Gy i The CNN convolutional neural network is trained for the target output, and the parameter optimization model is obtained after the training is completed.

[0042] Preferably, during the operation of the dust removal device, in each cycle t0 of the process parameter optimization model, the exhaust dust sensor collects N dust concentration values ​​in the exhaust gas, which are recorded as the actual dust concentration C S , N = 5-200; the parameter acquisition module obtains the detection result of the exhaust dust sensor and transmits it to the cleaning judgment module, and the cleaning judgment module judges whether cleaning is required in each cycle t0 according to the following method:

[0043] 1) Calculate the actual dust concentration C within the period t0 S Greater than the dust concentration target value C m The number of dust concentration data N1, if N1 / N ≥ 0.01 ~ 0.05, go to step 2), otherwise it is determined that cleaning is not required;

[0044] 2) Calculate the total time of each cycle t0 as △t, and divide a cycle into four time periods: 0~0.25△t, 0.25△t~0.5△t, 0.5△t~0.75△t, 0.75△t~△t, respectively recorded as time period 1, time period 2, time period 3, time period 4, calculate the actual dust concentration C in each time period S The average value of If satisfied If the dust is not removed, it is judged that dust removal is required; otherwise, it is judged that dust removal is not required;

[0045] 3) When the cleaning judgment module determines that cleaning is required, after the current cycle ends, the control module controls the cleaning operation in the following manner: control the gas flow controller to close, stop introducing flue gas, control the water flow controller to close, stop spraying atomized droplets, control the first ultrasonic transmitter to stop working, control the electrostatic precipitator host to stop working, control the second ultrasonic transmitter to work, and emit ultrasonic waves to clean the discharge electrode and the dust collecting electrode;

[0046] After the dust cleaning is completed, the first ultrasonic transmitter, the second ultrasonic transmitter and the electrostatic precipitator host are controlled to work according to the optimized process parameter Gy' obtained in the previous cycle, and the water flow controller is controlled to open, and finally the gas flow controller is controlled to open to re-introduce the flue gas.

[0047] The beneficial effects of the present invention are:

[0048] The present invention provides a dust removal device and a dust removal method for a flue gas electric heater. On the one hand, the present invention can intelligently adjust and optimize relevant process parameters according to the flue gas properties. On the other hand, the present invention can judge whether dust removal is required through characteristic indicators that can directly reflect the dust accumulation of the electrostatic precipitator, so that dust removal can be carried out in time to avoid the reduction of dust removal effect due to excessive dust accumulation.

[0049] The present invention adopts CNN convolutional neural network as the basic network model, and constructs a parameter optimization model by training a training data set composed of optimized process parameter data corresponding to different flue gas characteristic parameters and dust concentration target values ​​obtained in advance. The model can analyze the characteristic parameters of flue gas and the dust concentration target values ​​to obtain the process parameters with relatively lowest energy consumption under the condition of meeting the dust removal requirements, that is, the optimized process parameters. The dust removal operation of the dust removal device is controlled by the optimized process parameters, so that high-efficiency and low-energy dust removal can be achieved.

[0050] The present invention determines the timing of dust cleaning through the dust cleaning judgment module, which can realize timely dust cleaning and avoid a significant decrease in dust removal effect due to excessive dust accumulation. When dust cleaning is required, the control module controls the second ultrasonic transmitter to emit ultrasonic waves to clean the discharge electrode and the dust collecting electrode, which can avoid the defect that conventional mechanical vibration dust cleaning easily damages the electrode and has a better dust cleaning effect.

[0051] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1This is a schematic structural diagram of a dust removal device for a flue gas electric heater in Example 1;

[0053] Figure 2 is a principle block diagram of the controller in Example 1;

[0054] Figure 3 This is a flow chart of the dust removal method of the dust removal device for the flue gas electric heater in Example 2;

[0055] Figure 4 It is a flow chart for constructing the parameter optimization model in Example 2;

[0056] Figure 5 A flowchart of constructing a training data set Z in Example 2;

[0057] Figure 6 This is a flow chart of the dust cleaning judgment module in Example 2 for judging whether dust cleaning is required;

[0058] Figure 7 The actual dust concentration test results of the control case and the test case in the application test;

[0059] Figure 8 is the test result of the power consumption reference value η of the test case in the application test.

[0060] Description of reference numerals:

[0061] 1—smoke inlet pipe; 2—atomization box; 3—dust pre-agglomeration box; 4—ultrasonic electric field composite dust collector; 5—smoke exhaust pipe;

[0062] 11—gas flow controller; 12—humidity sensor; 13—temperature sensor; 14—intake dust sensor;

[0063] 21—atomizing nozzle; 22—water tank; 23—water pipe; 24—water flow controller;

[0064] 31 - first ultrasonic transmitter; 32 - guide vane; 33 - guide channel;

[0065] 41—electrostatic precipitator; 42—dust removal chamber; 43—electrostatic precipitator host; 44—discharge electrode; 45—dust collecting electrode; 46—dust collecting hopper; 47—second ultrasonic transmitter;

[0066] 51—Exhaust dust sensor;

[0067] 6—controller; 61—parameter acquisition module; 62—parameter optimization model; 63—ash cleaning judgment module; 64—control module. DETAILED DESCRIPTION

[0068] The present invention is further described in detail below in conjunction with embodiments so that those skilled in the art can implement the invention with reference to the description.

[0069] It should be understood that the terms such as “having”, “including” and “comprising” used herein do not exclude the existence or addition of one or more other elements or combinations thereof.

[0070] Example 1

[0071] Reference Figure 1-2 , this embodiment provides a dust removal device for a flue gas electric heater, comprising: a flue gas inlet pipe 1, an atomization box 2, a dust pre-agglomeration box 3, an ultrasonic electric field composite dust collector 4 and a flue gas exhaust pipe 5 connected in sequence along the airflow direction;

[0072] A first ultrasonic transmitter 31 is arranged in the dust pre-agglomeration box 3, and the ultrasonic electric field composite dust collector 4 includes an electrostatic precipitator 41 and a second ultrasonic transmitter 47 arranged in a dust removal chamber 42 of the electrostatic precipitator 41;

[0073] The flue gas intake pipe 1 is provided with a gas flow controller 11 , a humidity sensor 12 , a temperature sensor 13 and an intake dust sensor 14 , and the flue gas exhaust pipe 5 is provided with an exhaust dust sensor 51 .

[0074] In this embodiment, a plurality of atomizing nozzles 21 are arranged in the atomizing box 2, and all the atomizing nozzles 21 are connected to a water tank 22 arranged on the atomizing box 2 through a water pipe 23, so as to spray atomized droplets into the atomizing box 2. A water flow controller 1124 is arranged on the water pipe 23, so as to control the flow rate of water entering all the atomizing nozzles 21, and it can realize automatic control.

[0075] A plurality of guide channels 33 are formed inside the dust pre-agglomeration box 3 by a plurality of guide vanes 32 which are parallel to the axial direction of the dust pre-agglomeration box 3 and spaced apart from each other. Both ends of the guide vanes 32 are tapered tips.

[0076] The electrostatic precipitator 41 includes an electrostatic precipitator main unit 43 , a discharge electrode 44 and a dust collecting electrode 45 arranged in a dust removal chamber 42 , a dust collecting hopper 46 located below the electrostatic precipitator chamber 42 , and a second ultrasonic transmitter 47 is arranged in the electrostatic precipitator chamber 42 .

[0077] The flue gas entering the dust removal device enters from the flue gas inlet pipe 1, and then passes through the atomization box 2, the dust pre-agglomeration box 3, and the ultrasonic electric field composite dust collector 4 in sequence. After dust removal, it is discharged from the flue gas exhaust pipe 5 and enters subsequent treatment.

[0078] In the atomizing box 2, the purpose of spraying atomized droplets through the atomizing nozzle 21 is to use the atomized droplets as a carrier to promote the agglomeration of dust in the flue gas under the subsequent ultrasonic action. Acoustic agglomeration is to emit sound waves to make the dust in the gas vibrate within a certain range, so that it can collide with the surrounding particles, or even merge and agglomerate; by passing atomized droplets, the atomized droplets and dust particles agglomerate or collide, so that the dust contains liquid water, or the droplets contain dust particles. This prepares for the subsequent dust removal.

[0079] In the dust pre-agglomeration box 3, the dust in the flue gas is initially agglomerated by the action of ultrasound, and then enters the ultrasonic electric field composite dust collector 4, which can improve the subsequent dust removal efficiency in the ultrasonic electric field composite dust collector 4. The several guide channels 33 in the dust pre-agglomeration box 3 can help to evenly distribute the flue gas into the ultrasonic electric field composite dust collector 4, and can help to promote the pre-agglomeration of dust in the dust pre-agglomeration box 3.

[0080] In the ultrasonic electric field composite dust collector 4, the applied ultrasonic wave further promotes the agglomeration of dust in the flue gas, especially promotes the full agglomeration of fine dust in the flue gas, and through two ultrasonic waves, the dust is formed into aggregated particles with larger particle size, which can facilitate the efficient removal of dust by the electrostatic precipitator 41 (dust with small particle size is light in weight and has less charge under the electric field, so it is easily carried away by the airflow and not easily collected by the electric field, resulting in a low removal rate).

[0081] Therefore, in the present invention, the removal efficiency of dust in the flue gas by the electrostatic precipitator 41 can be significantly improved by combining the carrier effect of the atomized droplets and the auxiliary effect of the two-stage ultrasonic waves.

[0082] In this embodiment, the dust removal device also includes a controller 6 connected to the gas flow controller 11, the humidity sensor 12, the temperature sensor 13, the intake dust sensor 14, the water flow controller 1124, the first ultrasonic transmitter 31, the second ultrasonic transmitter 47, the electric dust removal host 43 and the exhaust dust sensor 51;

[0083] The controller 6 includes a parameter acquisition module 61, a parameter optimization model 62, a dust cleaning judgment module 63 and a control module 64. The parameter optimization model 62 is based on the characteristic parameter Fx of the flue gas introduced into the flue gas inlet pipe 1 and the preset dust concentration target value C in the exhaust gas discharged from the flue gas exhaust pipe 5. m , a machine learning algorithm is used to optimize the process parameter Gy of the dust removal device to obtain an optimized process parameter Gy', and the control module 64 controls the water flow controller 1124, the first ultrasonic transmitter 31, the second ultrasonic transmitter 47, and the electrostatic precipitator host 43 according to Gy';

[0084] The characteristic parameters Fx of the flue gas include the flue gas flow rate Qw entering the dust removal device, the relative humidity RH of the flue gas, the temperature T, the dust concentration C in the flue gas, and the dust particle size distribution parameter Lc, which is recorded as Fx = (Qw, RH, T, C, Lc); wherein the dust particle size distribution parameter Lc includes the concentration C of PM1.0 in the dust 1.0 、PM2.5 concentration C 2.5 and PM10.0 concentration C 10.0 ;

[0085] The process parameters Gy of the dust removal device include the atomized droplet ratio coefficient K, the power P1 and frequency f1 of the first ultrasonic transmitter 31, the power P2 and frequency f2 of the second ultrasonic transmitter 47, and the working voltage U of the electrostatic precipitator host 43, which are expressed as Gy = (K, P1, f1, P2, f2, U);

[0086] The atomized droplet ratio coefficient K is the ratio of the water flow Qs introduced into the water delivery pipe 23 to the flue gas flow Qw introduced into the flue gas inlet pipe 1, that is, K=Qs / Qw.

[0087] In this embodiment, the parameter optimization model 62 performs process parameter optimization in a cycle. The optimized process parameters are the characteristic parameter Fx of the currently introduced flue gas and the target value C of the dust concentration. m In the case of max When , the total power consumption W reaches the minimum;

[0088] The total power consumption W is the sum of the power consumption W1 of the first ultrasonic transmitter 31, the power consumption W2 of the second ultrasonic transmitter 47 and the power consumption W3 of the electrostatic precipitator host 43, that is, W=W1+W2+W3.

[0089] The dust removal device of the present invention is used for the front end of the flue gas electric heater to perform dust removal on the cold flue gas. The flue gas after dust removal is then heated and then enters the subsequent purification process. In other words, the processing object of the present invention is the cold flue gas that needs to be reheated later, and the temperature fluctuation range will be larger, which mainly depends on the previous processing operation. For example, in some embodiments, the temperature range is 50-150°C. The temperature of the flue gas will have a great influence on the efficiency of the electrostatic precipitator. For example: when the flue gas temperature is too low, in the electrostatic precipitator 41, due to insufficient energy, it is difficult for the particulate matter to obtain sufficient charge, resulting in the particulate matter being difficult to capture and the dust removal efficiency will be reduced.

[0090] Flue gas humidity will also affect the efficiency of electrostatic precipitator. When the humidity is too low, the dust agglomeration effect decreases, which is not conducive to its capture by the electrode. When the humidity is too high, water vapor will become a conductor in the air, increasing the conductivity of the air, which will cause the electric field strength to decrease, thereby affecting the overall performance of the electrostatic precipitator 41. In the present invention, the atomized droplet ratio coefficient K represents the amount of atomized droplets introduced into the flue gas, which can adjust the flue gas humidity and directly affect the number of atomized droplet carriers in the flue gas, so it also has a great influence on the dust removal efficiency.

[0091] In the two-stage ultrasonic operation, the main working parameters of the first ultrasonic transmitter 31 and the second ultrasonic transmitter 47: ultrasonic power and frequency (i.e., the frequency of the generated sound waves) are the main factors affecting the dust removal efficiency. In the electrostatic precipitator 41, its working voltage is the main parameter affecting the dust removal efficiency.

[0092] From the above analysis, it can be seen that the atomized droplet proportion coefficient K, the power P1 and frequency f1 of the first ultrasonic transmitter 31, the power P2 and frequency f2 of the second ultrasonic transmitter 47, and the working voltage U of the electrostatic precipitator host 43 will have a great influence on the dust removal efficiency of the dust removal device of the present invention. By optimizing these process parameters, the dust removal efficiency can be effectively improved, and energy consumption can be reduced while meeting the dust removal requirements, thereby reducing costs.

[0093] However, the effects of these process parameters on dust removal efficiency are nonlinear and mutually superimposed, and the relationship between process parameters and dust removal efficiency is relatively complex and difficult to accurately express through specific mathematical formulas. In the present invention, a parameter optimization model 62 based on a machine learning algorithm is adopted, and the relationship between process parameters and dust removal efficiency is analyzed and accumulated based on the previous training data. m A relatively accurate analysis can be performed to obtain the process parameters with the lowest energy consumption while meeting the dust removal requirements. These parameters are output as optimized process parameters, and the dust removal device is controlled to achieve efficient and low-energy dust removal.

[0094] In this embodiment, the dust cleaning judgment module 63 is used to judge whether it is necessary to clean the discharge electrode 44 and the dust collecting electrode 45 of the electrostatic precipitator 41. When cleaning is required, the control module 64 controls the second ultrasonic transmitter 47 to emit ultrasonic waves to clean the discharge electrode 44 and the dust collecting electrode 45. By judging the timing of cleaning by the dust cleaning judgment module 63, timely cleaning can be achieved to avoid a significant decrease in dust removal effect due to excessive dust accumulation. The method will be described in detail in the following embodiments.

[0095] Example 2

[0096] Reference Figure 3-6This embodiment provides a dust removal method for a dust removal device for a flue gas electric heater according to embodiment 1, comprising the following steps:

[0097] S1. During the operation of the dust removal device, the parameter optimization model 62 performs process parameter optimization in a cycle: First, the parameter acquisition module 61 obtains the following parameters of the flue gas currently introduced into the flue gas inlet pipe 1:

[0098] The flue gas flow rate Q detected by the gas flow controller 11 t , the relative humidity RH detected by the humidity sensor 12 t , the temperature T detected by the temperature sensor 13 t and the dust concentration C detected by the intake dust sensor 14 t and dust particle size distribution parameter Lc t , these parameters are used as the characteristic parameters Fx of the flue gas in the current cycle t t , denoted as Fx t =(Q t RH t , T t , C t , Lc t );

[0099] The parameter acquisition module 61 then obtains the preset dust concentration target value C m ;

[0100] S2, the parameter acquisition module 61 collects the characteristic parameters Fx of the flue gas t , Dust concentration target value C m Enter into the parameter optimization model 62 to obtain the optimized process parameter Gy' in the current cycle t t , Gy' t =(K' t 、P1' t 、f1' t 、P2' t 、f2' t , U' t );

[0101] K' t represents the optimized atomized droplet ratio coefficient, P1' t 、f1' t represent the optimized power and frequency of the first ultrasonic transmitter 31, P2′ t 、f2' t respectively represent the optimized power and frequency of the second ultrasonic transmitter 47, U' t represents the optimized working voltage of the electrostatic precipitator host 43;

[0102] S3, the control module 64 obtains the optimized process parameter Gy' according to the parameter optimization model 62 t , control so that in the current cycle t:

[0103] The ratio of the flow rate of water introduced into the water pipe 23 to the flow rate of flue gas introduced into the flue gas inlet pipe 1 is K' t , the power of the first ultrasonic transmitter 31 is P1' t , frequency is f1' t , the power of the second ultrasonic transmitter 47 is P2' t , frequency is f2' t , the working voltage of the electrostatic precipitator host 43 is U' t ;

[0104] S4. In the next cycle t+1, repeat steps S1-S3.

[0105] The parameter optimization model 62 performs process parameter optimization, that is, within a time period, control is performed according to the optimized process parameters obtained in the period until the next time period is optimized and updated again, and the time period t is conventionally selected according to actual conditions; for example, when the properties of the flue gas entering the dust removal device (including characteristic parameters such as flow rate, humidity, temperature, dust concentration, dust particle size distribution parameters, etc.) fluctuate slightly, a larger value can be selected; when the fluctuation is large, a smaller value is selected for the period, for example, the period is 5-240min. Furthermore, in one embodiment, the period is 30min; that is, the process can be optimized in time to ensure the dust removal effect, and the waste caused by excessive data processing of the parameter optimization model 62 can be avoided.

[0106] In this embodiment, the parameter optimization model 62 is constructed by the following method:

[0107] S1-1. Construct training data set Z:

[0108] S1-1-1. The flue gas is introduced into the dust removal device for treatment. The flue gas flow rate Qw and the target dust concentration C in the exhaust gas are fixed. m ;

[0109] Get different relative humidity RH i , Temperature T i , Dust concentration C i And the dust particle size distribution parameter Lc i Under this condition, the actual dust concentration in the exhaust gas (detected by the exhaust dust sensor 51) is not higher than the dust concentration target value C m When the total power consumption W reaches the minimum, the process parameter Gy i , the total power consumption at this time is W iThe total power consumption W is the sum of the power consumption W1 of the first ultrasonic transmitter 31, the power consumption W2 of the second ultrasonic transmitter 47 and the power consumption W3 of the electrostatic precipitator host 43, that is, W = W1 + W2 + W 3；

[0110] Among them, the process parameter Gy i Including the atomized droplet ratio coefficient K i , the power P1 of the first ultrasonic transmitter 31 i and frequency f1 i , the power P2 of the second ultrasonic transmitter 47 i and frequency f2 i , Working voltage U of electrostatic precipitator host 43 i ;

[0111] Qw、C m RH i , T i , C i , Lc i and the corresponding Gy i , W i Combine to get training data z i , thereby obtaining the corresponding flue gas flow Qw and dust concentration target value C m The n training data under, i = 1, 2, ..., n, n = 10-100; Lc i The concentration of PM1.0 in dust is included in C 1.0i 、PM2.5 concentration C 2.5i and PM10.0 concentration C 10.0i ;

[0112] In this embodiment, RH i The value is taken in the range of 5%-95%, T i The value is taken in the range of 50-150℃, C i 20-500 mg / m 3 Take values ​​within the range of

[0113] In this embodiment, the atomized droplet ratio coefficient K i The value is selected in the range of 0.01-0.2; P1 i The value is selected in the range of 100-1000W, f1 i The value is selected in the range of 10-100KHz; P2 i The value is selected in the range of 200-1500W, f2 i The value is selected in the range of 25-150KHz; U i (Operating voltage) is in the range of 3000V-15000V.

[0114] In this embodiment, a total of 80 pieces of training data are acquired within the above parameter range, that is, n=80.

[0115] S1-1-2. Change the value of the flue gas flow rate Qw and the dust concentration target value C m , use the same method as step S1-1-1 to obtain m training data z j ,i=1,2,...,m,m=50-5000;

[0116] In this embodiment, the flue gas flow rate Qw is 5000Nm 3 / h-30000Nm 3 / h range, C m 1-20 mg / m 3 The value of m is 800.

[0117] S1-1-3, all training data combinations are obtained to obtain a training data set Z;

[0118] S1-2. Model training:

[0119] Using the training data set Z, Qw, C m RH i , T i , C i , Lc i For access, Gy i The CNN convolutional neural network is trained for the target output, and after the training is completed, a parameter optimization model 62 is obtained.

[0120] The present invention adopts CNN convolutional neural network as the basic network model, and constructs a parameter optimization model 62 by training through a training data set composed of optimized process parameter data corresponding to different flue gas characteristic parameters and dust concentration target values ​​obtained in advance. It can analyze the characteristic parameters of the flue gas and the dust concentration target values ​​to obtain the process parameters with relatively lowest energy consumption while meeting the dust removal requirements, that is, the optimized process parameters. The dust removal operation of the dust removal device is controlled by the optimized process parameters, which can achieve efficient and low-energy dust removal.

[0121] In this embodiment, during the operation of the dust removal device, in each cycle t0 of the process parameter optimization model 62, the exhaust dust sensor 51 collects N dust concentration values ​​in the exhaust gas, which are recorded as the actual dust concentration C S, N=5-200, the value of N is selected according to the size of t0, for example, in one embodiment, t0=30min, N=100; the parameter acquisition module 61 obtains the detection result of the exhaust dust sensor 51 and transmits it to the cleaning judgment module 63, and the cleaning judgment module 63 judges whether cleaning is required in each cycle t0 according to the following method:

[0122] 1) Calculate the actual dust concentration C within the period t0 S Greater than the dust concentration target value C m The number of dust concentration data N1, if N1 / N ≥ 0.05, go to step 2), otherwise it is determined that cleaning is not required;

[0123] When the actual dust concentration C S There are a few values ​​greater than the dust concentration target value C m When the dust concentration is too high, it can be considered that it is an occasional high concentration caused by normal fluctuations, which does not mean that there is too much dust accumulation. However, if a large number (for example, more than 5) of them have actual dust concentration values ​​higher than the dust concentration target value C m When the dust concentration of the flue gas after the electrostatic precipitator 41 is too high, it should be considered that the dust accumulation on the electrodes (discharge electrode 44 and dust collecting electrode 45) of the electrostatic precipitator 41 may be too much, resulting in a significant decrease in dust removal efficiency, and a high dust concentration in the flue gas after the electrostatic precipitator 41 is dusted. Further judgment is required through step 2);

[0124] 2) Calculate the total time of each cycle t0 as △t, and divide a cycle into four time periods: 0~0.25△t, 0.25△t~0.5△t, 0.5△t~0.75△t, 0.75△t~△t, respectively recorded as time period 1, time period 2, time period 3, time period 4, calculate the actual dust concentration C in each time period S The average value of If satisfied If the dust is not removed, it is judged that dust removal is required; otherwise, it is judged that dust removal is not required;

[0125] When the actual dust concentration value is higher than the dust concentration target value C in step 1) mWhen there are too many data, this step is used to confirm whether there is too much dust accumulation. In this step, a cycle t0 is divided into 4 segments in chronological order to determine the actual dust concentration average value within a time period. If the average value shows a trend of continuous increase, it means that the actual dust concentration value as a whole shows a trend of gradually increasing over time. It can be considered that the electrodes (discharge electrode 44, dust collecting electrode 45) of the electrostatic precipitator 41 have too much dust accumulation, and the dust accumulation is increasing. The dust removal efficiency is significantly affected and needs to be cleaned. Otherwise, it is considered that the actual dust concentration value is in a normal fluctuation state and no cleaning is currently required. Determining whether cleaning is needed by combining step 2) with step 1) can prevent misjudgment and unnecessary cleaning, and can ensure timely cleaning.

[0126] 3) When the cleaning judgment module 63 determines that cleaning is required, after the current cycle ends, the control module 64 controls the cleaning operation in the following manner: control the gas flow controller 11 to close, stop the introduction of flue gas, control the water flow controller 1124 to close, stop spraying atomized droplets, control the first ultrasonic transmitter 31 to stop working, control the electrostatic precipitator host 43 to stop working, control the second ultrasonic transmitter 47 to work, and emit ultrasonic waves to clean the discharge electrode 44 and the dust collecting electrode 45; at this time, the power and frequency of the second ultrasonic transmitter 47 will be greater than their values ​​during normal dust removal operation. For example, in this embodiment, the power of the second ultrasonic transmitter 47 in the cleaning stage is 1200W and the frequency is 180KHz.

[0127] After the dust cleaning is completed, the first ultrasonic transmitter 31, the second ultrasonic transmitter 47 and the electrostatic precipitator host 43 are controlled to work according to the optimized process parameter Gy' obtained in the previous cycle, and the water flow controller 1124 is controlled to open, and finally the gas flow controller 11 is controlled to open and the flue gas is reintroduced.

[0128] That is to say, after each dust removal cycle, the dust cleaning judgment module 63 judges whether dust cleaning is needed. If not, the dust removal device continues to work normally and directly enters the next dust removal cycle; if the dust cleaning judgment module 63 judges that dust cleaning is needed, the second ultrasonic transmitter 47 emits ultrasonic waves, and the vibration generated by the ultrasonic waves causes the dust on the discharge electrode 44 and the dust collecting electrode 45 to fall off, and the dust is collected and discharged through the dust collecting hopper 46. This method judges the dust accumulation by directly reflecting the dust removal effect of the electrostatic precipitator 41. It can clean the discharge electrode 44 and the dust collecting electrode 45 in time to avoid a significant decrease in the dust removal effect due to excessive dust accumulation; and ultrasonic cleaning can avoid the defect that conventional mechanical vibration cleaning is easy to damage the electrode, and has a better cleaning effect.

[0129] Application Testing

[0130] The flue gas is cold asphalt flue gas from the aluminum forming workshop of a company. The flue gas properties are as follows: The flue gas flow rate is 10000-12000Nm 3 / h, the dust concentration in the flue gas is between 45-55mg / m 3 The temperature fluctuates within the range of 120-145℃, and the relative humidity fluctuates within the range of 10-25%.

[0131] The method of Example 2 is used to remove dust from the flue gas, and the dust concentration target value C is set. m =3mg / m 3 The main process parameters Gy at the initial stage are set as follows: atomized droplet proportion coefficient K=0.025, power P1=350W and frequency f1=45KHz of the first ultrasonic transmitter 31, power P2=900W and frequency f2=80KHz of the second ultrasonic transmitter 47, working voltage U=11000V of the electrostatic precipitator 41; power P2=1200W and frequency f2=180KHz of the second ultrasonic transmitter 47 in the cleaning stage.

[0132] A test example and a control example are set. The test example completely follows the method of Example 2, adopts the above-mentioned process parameter Gy as the initial parameter, and continuously optimizes the process parameter through the parameter optimization model 62 during the working process (t0=30min); in the control example, the above-mentioned process parameter Gy is kept unchanged; after running for 12 hours, a sampling index test is performed every 6 hours, and the average value of the actual dust concentration in the exhaust gas within 1 hour and the total power consumption W (the total power consumption W is the power consumption W1 of the first ultrasonic transmitter 31, the second ultrasonic transmitter 47 The power consumption W2 of the electric dust collector 43 and the power consumption W3 of the electric dust collector main unit 43, that is, W = W1 + W2 + W3), the total power consumption of the control example is 100%, and the power consumption reference value η of the test example is calculated, η = (total power consumption of the test example / total power consumption of the control example) * 100%; several sampling tests are carried out continuously, which are recorded as the first sampling time period (18-19h), the second sampling time period (24-25h), the third sampling time period (30-31h), and the fourth sampling time period (36-37h). The test results are shown in Tables 1 and Figure 7 , Figure 8 As shown:

[0133] Table 1

[0134]

[0135] It can be seen from the test results that by optimizing the process parameters in Example 2, energy consumption can be effectively reduced while ensuring the dust removal effect.

[0136] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A dust removal device for a flue gas electric heater, characterized in that: include: A flue gas inlet pipe, an atomization box, a dust pre-agglomeration box, an ultrasonic electric field composite dust collector and a flue gas exhaust pipe are sequentially connected along the airflow direction; A first ultrasonic transmitter is arranged in the dust pre-agglomeration box, and the ultrasonic electric field composite dust collector comprises an electrostatic precipitator and a second ultrasonic transmitter arranged in a dust removal chamber of the electrostatic precipitator; The flue gas intake pipe is provided with a gas flow controller, a humidity sensor, a temperature sensor and an intake dust sensor, and the flue gas exhaust pipe is provided with an exhaust dust sensor.

2. The dust removal device for flue gas electric heater according to claim 1, characterized in that: An atomizing nozzle is arranged in the atomizing box, and the atomizing nozzle is connected to a water tank arranged on the atomizing box through a water pipe for spraying atomized liquid droplets into the atomizing box. A water flow controller is arranged on the water pipe.

3. The dust removal device for flue gas electric heater according to claim 2, characterized in that: The electrostatic precipitator comprises an electrostatic precipitator main unit, a discharge electrode and a dust collecting electrode arranged in the dust removal chamber, and a dust collecting hopper located below the electrostatic precipitator chamber, and the second ultrasonic transmitter is arranged in the electrostatic precipitator chamber.

4. The dust removal device for flue gas electric heater according to claim 1, characterized in that: A plurality of guide channels are formed inside the dust pre-agglomeration box by a plurality of guide plates which are parallel to the axial direction of the dust pre-agglomeration box and are arranged at intervals from each other, and both ends of the guide plates are tapered tips.

5. The dust removal device for flue gas electric heater according to claim 3, characterized in that: The dust removal device also includes a controller connected to the gas flow controller, the humidity sensor, the temperature sensor, the intake dust sensor, the water flow controller, the first ultrasonic transmitter, the second ultrasonic transmitter, the electric dust removal host and the exhaust dust sensor; The controller includes a parameter acquisition module, a parameter optimization model, a dust cleaning judgment module and a control module. The parameter optimization model is based on the characteristic parameter Fx of the flue gas introduced into the flue gas inlet pipe and the preset dust concentration target value C in the exhaust gas discharged from the flue gas exhaust pipe. m , using a machine learning algorithm to optimize the process parameter Gy of the dust removal device to obtain an optimized process parameter Gy', and the control module controls the water flow controller, the first ultrasonic transmitter, the second ultrasonic transmitter, and the electrostatic precipitator host according to Gy'; The characteristic parameters Fx of the flue gas include the flue gas flow rate Qw entering the dust removal device, the relative humidity RH of the flue gas, the temperature T, the dust concentration C in the flue gas, and the dust particle size distribution parameter Lc, which is recorded as Fx = (Qw, RH, T, C, Lc); wherein the dust particle size distribution parameter Lc includes the concentration C of PM1.0 in the dust 1.0 、PM2.5 concentration C 2.5 and PM10.0 concentration C 10.0 ; The process parameters Gy of the dust removal device include the atomized droplet ratio coefficient K, the power P1 and frequency f1 of the first ultrasonic transmitter, the power P2 and frequency f2 of the second ultrasonic transmitter, and the working voltage U of the electrostatic precipitator host, which are denoted as Gy=(K, P1, f1, P2, f2, U); The atomized droplet ratio coefficient K is the ratio of the water flow Qs introduced into the water delivery pipe to the smoke flow Qw introduced into the smoke inlet pipe, that is, K=Qs / Qw.

6. The dust removal device for flue gas electric heater according to claim 5, characterized in that: The parameter optimization model optimizes the process parameters periodically. The optimized process parameters are the characteristic parameters Fx of the currently introduced flue gas and the target value C of the dust concentration. m In the case of max When , the total power consumption W reaches the minimum; The total power consumption W is the sum of the power consumption W1 of the first ultrasonic transmitter, the power consumption W2 of the second ultrasonic transmitter and the power consumption W3 of the electrostatic precipitator host, that is, W=W1+W2+W3.

7. The dust removal device for flue gas electric heater according to claim 1, characterized in that: The cleaning judgment module is used to judge whether it is necessary to clean the discharge electrode and the dust collecting electrode of the electrostatic precipitator. When cleaning is required, the control module controls the second ultrasonic transmitter to emit ultrasonic waves to clean the discharge electrode and the dust collecting electrode.

8. A dust removal method for a dust removal device for a flue gas electric heater according to any one of claims 5 to 7, characterized in that: The following steps are involved: S1. During the operation of the dust removal device, the parameter optimization model performs process parameter optimization in a cycle: first, the parameter acquisition module obtains the following parameters of the flue gas currently entering the flue gas inlet pipe: The flue gas flow rate Q detected by the gas flow controller t , relative humidity RH detected by humidity sensor t , the temperature T detected by the temperature sensor t And the dust concentration C detected by the intake dust sensor t and dust particle size distribution parameter Lc t , these parameters are used as the characteristic parameters Fx of the flue gas in the current cycle t t , denoted as Fx t =(Q t RH t , T t , C t , Lc t ); The parameter acquisition module then obtains the preset dust concentration target value C m ; S2, the parameter acquisition module collects the characteristic parameters Fx of the flue gas t , Dust concentration target value C m Enter into the parameter optimization model to obtain the optimized process parameter Gy' in the current cycle t t , Gy' t =(K' t 、P1' t 、f1' t 、P2' t 、f2' t , U' t ); K' t represents the optimized atomized droplet ratio coefficient, P1' t 、f1' t Respectively represent the power and frequency of the first ultrasonic transmitter obtained by optimization, P2' t 、f2' t Respectively represent the optimized power and frequency of the second ultrasonic transmitter, U' t Indicates the optimized working voltage of the electrostatic precipitator host; S3, the control module obtains the optimized process parameter Gy' according to the parameter optimization model t , control so that in the current cycle t: The ratio of the water flow rate in the water pipe to the flue gas flow rate in the flue gas inlet pipe is K' t , the power of the first ultrasonic transmitter is P1' t , frequency is f1' t , the power of the second ultrasonic transmitter is P2' t , frequency is f2' t , the working voltage of the electrostatic precipitator host is U' t ; S4. In the next cycle t+1, repeat steps S1-S3.

9. The dust removal device for a flue gas electric heater according to claim 8, characterized in that: The parameter optimization model is constructed by the following method: S1-1. Construct training data set Z: S1-1-1. The flue gas is introduced into the dust removal device for treatment. The flue gas flow rate Qw and the target dust concentration C in the exhaust gas are fixed. m ; Get different relative humidity RH i , Temperature T i , Dust concentration C i And the dust particle size distribution parameter Lc i Under this condition, the actual dust concentration in the exhaust gas should not exceed the dust concentration target value C m When the total power consumption W reaches the minimum, the process parameter Gy i , the total power consumption at this time is W i ; Among them, the process parameter Gy i Including the atomized droplet ratio coefficient K i , the power P1 of the first ultrasonic transmitter i and frequency f1 i , the power of the second ultrasonic transmitter P2 i and frequency f2 i , Working voltage U of the electrostatic precipitator host i ; Qw、C m RH i , T i , C i , Lc i and the corresponding Gy i , W i Combine to get training data z i , thereby obtaining the corresponding flue gas flow Qw and dust concentration target value C m The n training data under, i = 1, 2, ..., n, n = 10-100; S1-1-2. Change the value of the flue gas flow rate Qw and the dust concentration target value C m , use the same method as step S1-1-1 to obtain m training data z j ,i=1,2,...,m,m=50-5000; S1-1-3, all training data combinations are obtained to obtain a training data set Z; S1-2. Model training: Using the training data set Z, Qw, C m RH i , T i , C i , Lc i For access, Gy i The CNN convolutional neural network is trained for the target output, and the parameter optimization model is obtained after the training is completed.

10. The dust removal device for flue gas electric heater according to claim 8, characterized in that: During the operation of the dust removal device, in each cycle t0 of the process parameter optimization model, the exhaust dust sensor collects N dust concentration values ​​in the exhaust gas, which are recorded as the actual dust concentration C S , N = 5-200; the parameter acquisition module obtains the detection result of the exhaust dust sensor and transmits it to the cleaning judgment module, and the cleaning judgment module judges whether cleaning is required in each cycle t0 according to the following method: 1) Calculate the actual dust concentration C within the period t0 S Greater than the dust concentration target value C m The number of dust concentration data N1, if N1 / N ≥ 0.01 ~ 0.05, go to step 2), otherwise it is determined that cleaning is not required; 2) Calculate the total time of each cycle t0 as △t, and divide a cycle into four time periods: 0~0.25△t, 0.25△t~0.5△t, 0.5△t~0.75△t, 0.75△t~△t, respectively recorded as time period 1, time period 2, time period 3, time period 4, calculate the actual dust concentration C in each time period S The average value of If satisfied If the dust is not removed, it is judged that dust removal is required; otherwise, it is judged that dust removal is not required; 3) When the cleaning judgment module determines that cleaning is required, after the current cycle ends, the control module controls the cleaning operation in the following manner: control the gas flow controller to close, stop introducing flue gas, control the water flow controller to close, stop spraying atomized droplets, control the first ultrasonic transmitter to stop working, control the electrostatic precipitator host to stop working, control the second ultrasonic transmitter to work, and emit ultrasonic waves to clean the discharge electrode and the dust collecting electrode; After the dust cleaning is completed, the first ultrasonic transmitter, the second ultrasonic transmitter and the electrostatic precipitator host are controlled to work according to the optimized process parameter Gy' obtained in the previous cycle, and the water flow controller is controlled to open, and finally the gas flow controller is controlled to open to re-introduce the flue gas.

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